Abstract
The problem of imbalanced data sets occurs anytime one class represents a circumscribed concept, while the other represents the counterpart of that concept. The imbalanced data set problem can thus take two distinct forms: either the counterpart class is under-sampled relative to the concept class or it is over-sampled but particularly sparse. In bankruptcy prediction, classifiers are faced with imbalanced datasets: a lot of healthy firms and a smaller number of bankrupt firms. This paper firstly provides a systematic study on the various methodologies that have tried to handle the problem of imbalanced datasets. It presents an experimental study of these methodologies with a proposed technique and it concludes that such a framework can be a more effective solution to the bankruptcy prediction. Our method seems to allow improved identification of difficult small class (bankrupt firms) in predictive analysis, while keeping the classification ability of the other class (healthy firms) in an acceptable level.